Inferring Causal Relations from Observational Data
May 17, 2023
Mental disorder is produced by direct causal interactions between symptoms that reinforce each other via feedback loops. (Borsboom & Cramer, 2013).
Directed Acyclic Graph (DAG)
Directed Cyclic Graph (DCG)
Problem: Estimating a cyclic causal model is fundamentally very difficult.
Cyclic Causal Discovery (CCD) (Richardson, 1996)
Fast Causal Inference (FCI) (M. Mooij & Claassen, 2020)
Cyclic Causal Inference (CCI) (Strobl, 2019)
Causal inference is the fundamental interest in science.
The underlying dynamic processes of many systems contain cycles.
Our study showcases the cyclic causal discovery algorithms that are potentially suitable for typical psychological observational data.
Conclusion is rather nuanced (no one-size-fits-all algorithm).
Causal discovery methods could provide much richer insights into the underlying causal dynamics of the system than statistical network models.
Example:
For the arrow head (\(>\)): precision = \(\frac{4}{4 + 3 + 0}\) and recall = \(\frac{4}{4 + 0 + 0}\).
The value of SHD for the example PAG output from (b) – provided that the true ancestral graph is (a) – is 6: 0 (A) + 0 (D) + 6 (C).
(a): True ancestral graph
(b): PAG estimated by CCD
(c): PAG estimated by FCI
(d): PAG estimated by CCI
Personalized psychotherapy (target symptoms)
Medical: effective treatment design
Possible combination with different types of causal discovery algorithm. \(\rightarrow\) Hybrid!
CCD+ GES (greedy equivalence search)
Methodology and Statistics for the Behavioural, Biomedical and Social Sciences